AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.594 | 0.450 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.000104 |
| Time: | 22:00:08 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -46.5637 | 137.169 | -0.339 | 0.738 | -333.662 240.535 |
| C(dose)[T.1] | 112.1240 | 182.029 | 0.616 | 0.545 | -268.868 493.116 |
| expression | 12.6908 | 17.257 | 0.735 | 0.471 | -23.429 48.811 |
| expression:C(dose)[T.1] | -7.2324 | 23.220 | -0.311 | 0.759 | -55.832 41.367 |
| Omnibus: | 0.948 | Durbin-Watson: | 1.819 |
| Prob(Omnibus): | 0.623 | Jarque-Bera (JB): | 0.758 |
| Skew: | -0.012 | Prob(JB): | 0.684 |
| Kurtosis: | 2.111 | Cond. No. | 435. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.34 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.11e-05 |
| Time: | 22:00:08 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.8415 | 89.787 | -0.165 | 0.870 | -202.134 172.451 |
| C(dose)[T.1] | 55.5006 | 9.087 | 6.108 | 0.000 | 36.546 74.456 |
| expression | 8.6959 | 11.282 | 0.771 | 0.450 | -14.839 32.230 |
| Omnibus: | 1.261 | Durbin-Watson: | 1.855 |
| Prob(Omnibus): | 0.532 | Jarque-Bera (JB): | 0.866 |
| Skew: | 0.047 | Prob(JB): | 0.648 |
| Kurtosis: | 2.054 | Cond. No. | 166. |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 38.84 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:00:08 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 21 | BIC: | 208.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
| C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.060 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.023 |
| Method: | Least Squares | F-statistic: | 0.5046 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.485 |
| Time: | 22:00:08 | Log-Likelihood: | -112.83 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 178.2008 | 138.827 | 1.284 | 0.213 | -110.507 466.908 |
| expression | -12.5913 | 17.726 | -0.710 | 0.485 | -49.454 24.272 |
| Omnibus: | 3.936 | Durbin-Watson: | 2.505 |
| Prob(Omnibus): | 0.140 | Jarque-Bera (JB): | 1.661 |
| Skew: | 0.272 | Prob(JB): | 0.436 |
| Kurtosis: | 1.801 | Cond. No. | 155. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 6.078 | 0.030 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.635 |
| Model: | OLS | Adj. R-squared: | 0.535 |
| Method: | Least Squares | F-statistic: | 6.366 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00924 |
| Time: | 22:00:08 | Log-Likelihood: | -67.751 |
| No. Observations: | 15 | AIC: | 143.5 |
| Df Residuals: | 11 | BIC: | 146.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -666.4142 | 432.819 | -1.540 | 0.152 | -1619.041 286.213 |
| C(dose)[T.1] | 132.0761 | 586.133 | 0.225 | 0.826 | -1157.993 1422.145 |
| expression | 85.4687 | 50.396 | 1.696 | 0.118 | -25.453 196.390 |
| expression:C(dose)[T.1] | -7.6062 | 69.091 | -0.110 | 0.914 | -159.675 144.463 |
| Omnibus: | 0.684 | Durbin-Watson: | 0.823 |
| Prob(Omnibus): | 0.710 | Jarque-Bera (JB): | 0.638 |
| Skew: | -0.409 | Prob(JB): | 0.727 |
| Kurtosis: | 2.407 | Cond. No. | 1.02e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.634 |
| Model: | OLS | Adj. R-squared: | 0.573 |
| Method: | Least Squares | F-statistic: | 10.40 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00240 |
| Time: | 22:00:08 | Log-Likelihood: | -67.759 |
| No. Observations: | 15 | AIC: | 141.5 |
| Df Residuals: | 12 | BIC: | 143.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -631.6673 | 283.711 | -2.226 | 0.046 | -1249.821 -13.513 |
| C(dose)[T.1] | 67.5717 | 14.832 | 4.556 | 0.001 | 35.255 99.888 |
| expression | 81.4219 | 33.025 | 2.465 | 0.030 | 9.466 153.377 |
| Omnibus: | 0.654 | Durbin-Watson: | 0.798 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.592 |
| Skew: | -0.403 | Prob(JB): | 0.744 |
| Kurtosis: | 2.453 | Cond. No. | 382. |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 10.58 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:00:08 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 13 | BIC: | 147.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
| C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
| Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01648 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.900 |
| Time: | 22:00:08 | Log-Likelihood: | -75.291 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.4126 | 383.825 | 0.116 | 0.910 | -784.791 873.617 |
| expression | 5.8180 | 45.323 | 0.128 | 0.900 | -92.096 103.732 |
| Omnibus: | 0.782 | Durbin-Watson: | 1.660 |
| Prob(Omnibus): | 0.677 | Jarque-Bera (JB): | 0.649 |
| Skew: | 0.086 | Prob(JB): | 0.723 |
| Kurtosis: | 1.996 | Cond. No. | 325. |